Design and manufacturing test of electronic systems require consideration of the effect of variation of individual components on the variability of the system. Similarly, design and manufacturing test of integrated circuit components require consideration of the effect of variation of individual circuit modules on the variation of the integrated circuit itself. Characterizing and modeling this variation support decision-making in the design and manufacturing test of both electronic systems and integrated circuit components.
Test in the manufacture of electronic systems or integrated circuit components requires specification of test conditions such as temperatures, voltages, frequencies and parametric test limits, as well as end-use specifications. End-use specifications are given in a datasheet specification document used by designers of systems employing the electronic (sub-) system or integrated circuit component being manufactured. Test specifications and datasheet specifications are set to optimize yield, yet meet quality and reliability requirements, and so have an important revenue and brand image impact. Each unit tested is characterized by many parametric attributes such as power and delay for electronic systems, or Isb, Fmax, bit refresh time, reliability lifetimes, etc., for integrated circuit components, all as functions of environmental conditions such as temperature, voltage and frequency. These parametric attributes are dependent (correlated) to various degrees in the population of manufactured units. A traditional method of optimizing the test manufacturing flow is to characterize a sufficiently large sample of units of a specific product by measuring, but not screening, the multiple parametric attributes over a range of temperatures, voltages, and frequencies corresponding to possible Test and Use conditions of a future product. Test set points and limits are then found by filtering the data and computing figures of merit (FOMs) such as yield loss, overkill, and end-use fail fraction so that manufacturing cost, and quality and reliability targets are met. What is needed is a way to do this earlier in the product lifecycle by building a statistical model of a product from test vehicle data and then using the model to scale the model to the specific die area, bit count, fault tolerance scheme, etc. of a future product. The same statistical model may also be used later in the product lifecycle to decide on end-use specifications to be published to system designers using the component, and even later in the product lifecycle to optimize the test specification in manufacturing test. A test vehicle is an electronic subsystem or integrated circuit device specifically designed to facilitate data acquisition needed to build the statistical model. The statistical model must handle multi-variate dependency, and be scalable from the conditions of the test vehicle to the hypothetical design and manufacturing specifications of a future product.